HolmesGPT Skill

AI-powered troubleshooting for Kubernetes and cloud-native environments.

Overview

HolmesGPT is a CNCF Sandbox project that connects AI models with live observability data to investigate infrastructure problems, find root causes, and suggest remediations. It operates with read-only access and respects RBAC permissions, making it safe for production environments.

Quick Reference

Key Features

• Root Cause Analysis: Investigates alerts and cluster issues
• Multi-Source Integration: 30+ toolsets (K8s, Prometheus, Grafana)
• Alert Integration: AlertManager, PagerDuty, OpsGenie, Jira, Slack
• Interactive Mode: Troubleshooting with /run, /show, /clear
• Custom Toolsets: Extend with proprietary tools via YAML configuration
• CI/CD Integration: Automated deployment failure investigation

Installation Quick Start

CLI (Homebrew)

brew tap robusta-dev/homebrew-holmesgpt
brew install holmesgpt
export ANTHROPIC_API_KEY="your-key"  # or OPENAI_API_KEY
holmes ask "what pods are unhealthy?"

Kubernetes (Helm)

helm repo add robusta https://robusta-charts.storage.googleapis.com
helm repo update
helm install holmesgpt robusta/holmes -f values.yaml

Docker

docker run -it --net=host \
  -e OPENAI_API_KEY="your-key" \
  -v ~/.kube/config:/root/.kube/config \
  us-central1-docker.pkg.dev/genuine-flight-317411/devel/holmes \
  ask "what pods are crashing?"

Essential Commands

# Basic investigation
holmes ask "what pods are unhealthy and why?"
holmes ask "why is my deployment failing?"

# Interactive mode
holmes ask "investigate issue" --interactive

# Alert investigation
holmes investigate alertmanager --alertmanager-url http://localhost:9093
holmes investigate pagerduty --pagerduty-api-key <KEY> --update

# With file context
holmes ask "summarize the key points" -f ./logs.txt

# CI/CD integration
holmes ask "why did deployment fail?" --destination slack --slack-token <TOKEN>

Supported AI Providers

Basic Helm Values Structure

# values.yaml for Kubernetes deployment
image:
  repository: robustadev/holmes
  tag: latest

env:
  - name: ANTHROPIC_API_KEY
    valueFrom:
      secretKeyRef:
        name: holmesgpt-secrets
        key: anthropic-api-key

# Model configuration
modelList:
  sonnet:
    api_key: "{{ env.ANTHROPIC_API_KEY }}"
    model: anthropic/claude-sonnet-4-20250514
    temperature: 0

# Toolsets to enable
toolsets:
  kubernetes/core:
    enabled: true
  kubernetes/logs:
    enabled: true
  prometheus/metrics:
    enabled: true

# Resources
resources:
  requests:
    memory: "1024Mi"
    cpu: "100m"
  limits:
    memory: "1024Mi"

# RBAC (read-only by default)
createServiceAccount: true

Interactive Mode Commands

Custom Toolset Example

# custom-toolset.yaml
toolsets:
  my-custom-tool:
    description: "Custom diagnostic tool"
    tools:
      - name: check_service_health
        description: "Check health of a specific service"
        command: |
          curl -s http://{{ service_name }}.{{ namespace }}.svc.cluster.local/health
        parameters:
          - name: service_name
            description: "Name of the service"
          - name: namespace
            description: "Kubernetes namespace"

Use with: holmes ask "check health" -t custom-toolset.yaml

Kubernetes Annotations for Integration

# Add to Services/Deployments for HolmesGPT context
metadata:
  annotations:
    holmesgpt.dev/runbook: |
      This service handles payment processing.
      Common issues: database connectivity, API rate limits.
      Check: kubectl logs -l app=payment-service

Environment Variables Reference

Best Practices

1. Use Specific Queries: Include namespace, deployment name, symptoms
2. Start with Claude Sonnet 4.0/4.5: Best accuracy for complex investigations
3. Enable Relevant Toolsets: Only enable what you need to reduce noise
4. Use Interactive Mode: For complex multi-step investigations
5. Set Up Runbooks: Provide context for known alert types
6. CI/CD Integration: Automate deployment failure analysis

Security Considerations

• HolmesGPT uses read-only access (get, list, watch only)
• Respects existing RBAC permissions
• Never modifies, creates, or deletes resources
• API keys stored in Kubernetes Secrets
• Data not used for model training

Official Resources

• Documentation: https://holmesgpt.dev/
• GitHub: https://github.com/robusta-dev/holmesgpt
• Helm Chart: https://github.com/robusta-dev/holmesgpt/tree/master/helm/holmes
• Slack Community: Cloud Native Slack

Gotchas

• Read-only RBAC means HolmesGPT can't see Secrets by default: Investigations involving misconfigured Secret refs return "no permission to read" even though the agent flags it as a possible cause. Either grant secrets:get on a specific namespace or accept the blind spot — don't broaden cluster-wide.
• Toolset enablement is cumulative and noisy at scale: Enabling all 30+ toolsets makes the LLM scan irrelevant data and dilutes accuracy. Enable only the toolsets matching your stack — every extra one costs tokens and adds noise to root-cause analysis.
• Model temperature MUST be 0 for reproducible investigations: Default Helm values sometimes ship with temperature > 0; same alert gives different root causes across runs. Pin temperature: 0 in modelList or compare results between runs and lose trust.
• AlertManager URL must be reachable from the HolmesGPT pod, not the CLI: holmes investigate alertmanager --alertmanager-url http://localhost:9093 works from a laptop but fails inside the cluster — use the in-cluster service DNS (http://kube-prometheus-stack-alertmanager.monitoring:9093).
• /clear doesn't reset toolset context, only conversation history: Cached tool outputs from prior investigation persist within the session. Long interactive sessions accumulate stale Prometheus data that contaminates new questions. Restart the CLI between unrelated incidents.
• Anthropic model names in modelList need the anthropic/ prefix: model: claude-sonnet-4-20250514 fails silently with provider-not-found; correct form is model: anthropic/claude-sonnet-4-20250514. LiteLLM error message says "model not found" without naming the missing prefix.